A multi-port interconnected low-voltage distribution network loss optimization method and related devices

Abstract

This application relates to the field of distribution network optimization technology, and specifically provides a multi-port interconnected low-voltage distribution network loss optimization method and related devices. By introducing the population improvement rate as an evaluation index for the optimization efficiency and quality of the CSO algorithm during iterative calculation, and accordingly improving the traditional CSO algorithm, the problem of the algorithm falling into a local optimal solution can be avoided, and then a transformer load rate allocation scheme with the minimum loss can be determined, and the analysis efficiency can be improved. In this way, the line loss management efficiency and quality can be improved, and further the distribution network loss can be reduced to achieve optimization.

Description

Technical Field

[0001] This application relates to the technical field of distribution network optimization, and particularly to a method for optimizing the losses of a multi-port interconnected low-voltage distribution network and related devices. Background Art

[0002] The distribution network is an important part of the power system. The losses of the distribution network are directly related to the power utilization rate, economic cost, and environmental cost of the power system. As one of the important devices in the distribution network, the losses of the distribution transformer are closely related to the losses of the distribution network and are an important link in the line loss management of the distribution network. The transformer losses change with the load rate of the distribution transformer. When the transformer capacity is too small and the load is too large, the distribution transformer will enter the heavy-load and over-load states, increasing the transformer losses. When the transformer capacity is too large and the load is too small, there will be a problem of wasted capacity in the distribution transformer, increasing the operating cost of the distribution network.

[0003] To attempt to reduce the losses and operating costs of the distribution network, the prior art has proposed using the traditional CSO (Crisscross Optimization Algorithm) for sub-load calculation to reduce the transformer losses by reducing the transformer load rate.

[0004] However, through research by the inventor, it is found that although the traditional CSO algorithm has a high search speed and search progress, it has the common local optimum problem of optimization algorithms. Therefore, it is impossible to determine the transformer load rate allocation scheme with the minimum losses, and thus it is impossible to further reduce the losses of the distribution network. Summary of the Invention

[0005] The purpose of this application aims to at least solve one of the above technical defects, especially the technical defect that the losses of the distribution network cannot be further reduced in the prior art.

[0006] In a first aspect, an embodiment of this application provides a method for optimizing the losses of a multi-port interconnected low-voltage distribution network. The multi-port interconnected low-voltage distribution network includes at least two ports and at least two distribution transformers. The method includes:

[0007] Determine the parental population of the current iteration round;

[0008] According to the transformer operation data of each distribution transformer, the port operation data of each port, and the parental population of the current iteration round, use the Crisscross Optimization Algorithm (CSO) to update the population and obtain the offspring population of the current iteration round;

[0009] Calculate the population improvement rate of the current iteration round based on the parental population of the current iteration round and the offspring population of the current iteration round; wherein, the population improvement rate is used to reflect the improvement degree of the offspring population relative to the parental population in the current iteration round; the population improvement rate is positively correlated with the improvement degree;

[0010] If the population improvement rate is less than the preset improvement rate threshold, when the preset population initialization rule is satisfied, initialize the parental population of the current iteration round, and use the initialized parental population as the parental population of the next iteration round, and enter the next iteration round;

[0011] If the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not satisfied and the preset iteration end condition is not satisfied, use the parental population of the current iteration round as the parental population of the next iteration round, and enter the next iteration round;

[0012] If the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not satisfied and the iteration end condition is satisfied, determine the target load rate corresponding to each distribution transformer according to the parental population of the current iteration round, and control the operation of each distribution transformer based on each target load rate.

[0013] In some embodiments, the parental population of the current iteration round includes m first transformer load distribution schemes, and the offspring population of the current iteration round includes m second transformer load distribution schemes, where m is a preset positive integer;

[0014] The calculating the population improvement rate of the current iteration round based on the parental population of the current iteration round and the offspring population of the current iteration round includes:

[0015] Determine the first optimal load scheme among the m first transformer load distribution schemes, and determine the second optimal load scheme among the m second transformer load distribution schemes;

[0016] According to the preset distribution network loss objective function, determine the first fitness corresponding to the first optimal load scheme;

[0017] According to the distribution network loss objective function, determine the second fitness corresponding to the second optimal load scheme;

[0018] Based on the first fitness and the second fitness, calculate the population improvement rate of the current iteration round.

[0019] In some embodiments, the calculating the population improvement rate of the current iteration round based on the first fitness and the second fitness includes:

[0020] The population improvement rate of the current iteration round is calculated based on the following expression :

[0021]

[0022] In the formula, is the first optimal load scheme, is the second optimal load scheme, is the first fitness, is the second fitness, is and the Euclidean distance between, is a preset constant.

[0023] In some embodiments, the distribution network loss objective function is:

[0024]

[0025] In the formula, is the distribution network loss objective function, D is the number of distribution transformers, is the iron loss of the i-th distribution transformer, is the load rate of the i-th distribution transformer, is the copper loss of the i-th distribution transformer, is the active power loss of the j-th port, is the penalty coefficient, is the received power of the power supply equipment corresponding to the i-th distribution transformer, is the load demand of the power supply equipment corresponding to the i-th distribution transformer.

[0026] In some embodiments, the population initialization rule is that the number of population initializations is less than or equal to a preset initialization number threshold; and / or, the iteration end condition is that the number of iterations is greater than or equal to a preset iteration number threshold.

[0027] In some embodiments, the method further includes:

[0028] If the population improvement rate is greater than or equal to the preset improvement rate threshold, when the iteration end condition is not satisfied, the offspring population of the current iteration round is used as the parent population of the next iteration round, and the next iteration round is entered.

[0029] In some embodiments, the method further includes:

[0030] If the population improvement rate is greater than or equal to the preset improvement rate threshold, when the iteration end condition is satisfied, the target load rate corresponding to each distribution transformer is determined according to the offspring population of the current iteration round, and each distribution transformer is controlled to operate based on each target load rate.

[0031] In a second aspect, an embodiment of the present application provides a multi-port interconnected low-voltage distribution network loss optimization device. The multi-port interconnected low-voltage distribution network includes at least two ports and at least two distribution transformers. The device includes:

[0032] A parental population determination module, configured to determine the parental population of the current iteration round;

[0033] A CSO update module, configured to perform population update on the parental population of the current iteration round by using the cross-swarm optimization algorithm CSO according to the transformer operation data of each distribution transformer, the port operation data of each port, and the parental population of the current iteration round, and obtain the offspring population of the current iteration round;

[0034] An improvement rate calculation module, configured to calculate the population improvement rate of the current iteration round based on the parental population and the offspring population of the current iteration round; wherein, the population improvement rate is used to reflect the improvement degree of the offspring population relative to the parental population in the current iteration round; the population improvement rate is positively correlated with the improvement degree;

[0035] A first iteration module, configured to, if the population improvement rate is less than the preset improvement rate threshold, when the preset population initialization rule is satisfied, perform population initialization on the parental population of the current iteration round, use the initialized parental population as the parental population of the next iteration round, and enter the next iteration round;

[0036] A second iteration module, configured to, if the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not satisfied and the preset iteration end condition is not satisfied, use the parental population of the current iteration round as the parental population of the next iteration round, and enter the next iteration round;

[0037] A first transformer control module, configured to, if the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not satisfied and the iteration end condition is satisfied, determine the target load rate corresponding to each distribution transformer according to the parental population of the current iteration round, and control each distribution transformer to operate based on each target load rate.

[0038] In a third aspect, an embodiment of the present application provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to execute the steps of the multi-port interconnected low-voltage distribution network loss optimization method described in any of the above embodiments.

[0039] In a fourth aspect, an embodiment of the present application provides a computer device, which includes: one or more processors, and a memory;

[0040] The memory stores computer-readable instructions, which, when executed by the one or more processors, execute the steps of the multi-port interconnected low-voltage distribution network loss optimization method described in any of the above embodiments.

[0041] In a multi-port interconnected low-voltage distribution network loss optimization method and related device provided in some embodiments of the present application, when using the CSO algorithm to optimize the load factor of each distribution transformer and the transmission power of each port in the region, if the population improvement rate in the current iteration round is less than the preset improvement rate threshold, it indicates that the relative position relationship between samples may have inhibited the search process and may fall into a local optimal solution. Therefore, under the condition of meeting the population initialization rule, the present application can initialize the parent population, so that the neighborhoods of all samples will be randomly redistributed, that is, the relative position relationship of the samples is changed, and the next iteration round is entered. In this way, perturbations can be added and bad search directions can be avoided in time to prevent falling into a local optimal solution.

[0042] If the population improvement rate is less than the preset improvement rate threshold and at the same time does not meet the population initialization rule and the iteration end condition, the population can be not updated, and the parent population in the current iteration round is used as the parent population in the next iteration round, and the next iteration round is entered. In this way, the optimal solution after the last update can be fully utilized through the backtracking operation, so as to avoid the algorithm falling into a local optimal solution and avoid repeated optimization of the algorithm, which helps to improve the solution efficiency.

[0043] In summary, the present application introduces the population improvement rate as an evaluation index for the optimization efficiency and quality of the CSO algorithm during iterative calculation, and improves the traditional CSO algorithm accordingly, so as to avoid the problem of the algorithm falling into a local optimal solution, and then determine the transformer load factor allocation scheme with the minimum loss and improve the analysis efficiency. In this way, the line loss management efficiency and quality can be improved, and then the distribution network loss can be reduced to achieve optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0045] Figure 1 In one embodiment, it is a network topology diagram of a multi-port interconnected low-voltage distribution network;

[0046] Figure 2 In one embodiment, it is one of the flow schematic diagrams of the loss optimization method for a multi-port interconnected low-voltage distribution network;

[0047] Figure 3 In one embodiment, it is another flow schematic diagram of the loss optimization method for a multi-port interconnected low-voltage distribution network;

[0048] Figure 4 In one embodiment, it is a structural schematic diagram of a loss optimization device for a multi-port interconnected low-voltage distribution network;

[0049] Figure 5 In one embodiment, it is an internal structure diagram of a computer device. Detailed implementation manners

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0051] Currently, the low-voltage distribution network generally adopts a single radial operation mode. With the continuous development of power electronics technology, for some low-voltage distribution networks with high power supply reliability, their low-voltage distribution network closed-loop construction has been carried out, enabling the low-voltage distribution network to operate in a looped manner. Exemplarily, Figure 1 shows the network topology diagram of the regional voltage distribution network based on multi-port flexible switches. As Figure 1 shown, the multi-port interconnected low-voltage distribution network may include multiple ports and multiple distribution transformers. The multiple distribution transformers can be connected through multiple ports and can achieve looped operation through a flexible loop closing device.

[0052] In some embodiments, the present application provides a loss optimization method for a multi-port interconnected low-voltage distribution network, which can Figure 1Optimize the losses of the multi-port interconnected low-voltage distribution network shown below. The following embodiments are described by taking the application of this method to a computer device as an example. It can be understood that the computer device of the present application can be any device with data processing functions, which can be but is not limited to devices such as servers, desktop computers, laptop computers, notebook computers, tablet computers, smart phones, etc. The present application does not make specific restrictions on this.

[0053] As Figure 2 shown, a method for optimizing the losses of a multi-port interconnected low-voltage distribution network provided by the present application may include the following steps:

[0054] S202: Determine the parental population of the current iteration round.

[0055] Specifically, the present application can utilize the adaptive memory search strategy and the dynamic topology improved cross search optimization (CSO) algorithm, and use the improved CSO algorithm to solve the optimization model of the multi-port interconnected low-voltage distribution network considering port losses and port capacities, so as to obtain the optimal operating load of each distribution transformer and the optimal transmission power of each port, making the losses of the low-voltage distribution network reach the minimum.

[0056] During the process of solving using the improved CSO algorithm, population optimization can be performed through multiple iterations to optimize the load distribution scheme of the distribution transformers. In each iteration round, the computer device can execute the steps of S202 to S212.

[0057] In the current iteration round, the computer device can first determine the parental population of the current iteration round. It can be understood that the parental population can include one or more particles. Each particle can be a transformer load distribution scheme, used to indicate the load that each distribution transformer in the low-voltage distribution network needs to bear. In some examples, if the current iteration round is the first iteration round, a preset population initialization algorithm can be used to generate the parental population of the current iteration round.

[0058] S204: According to the transformer operation data of each distribution transformer, the port operation data of each port, and the parental population of the current iteration round, use the cross search optimization (CSO) algorithm to update the population and obtain the offspring population of the current iteration round.

[0059] Among them, the transformer operation data can be used to reflect the operation performance of the distribution transformer, including but not limited to at least one of the basic operation data such as the maximum capacity of the distribution transformer and the transformer loss. The port operation data can be used to reflect the current-carrying capacity and loss situation of the port, including but not limited to at least one of the data such as the maximum current-carrying capacity of the port and the corresponding loss parameters.

[0060] In this step, the computer device can pre-acquire the transformer operation data of each distribution transformer and the port operation data of each port. Further, the computer device can also pre-collect or real-time collect the load demands of each substation area.

[0061] In the process of population update, the computer device can use the cross-swarm optimization (CSO) algorithm to update the population according to the transformer operation data of each transformer, the port operation data of each port, and the parent population of the current iteration round, and obtain the offspring population of the current iteration round. It can be understood that similar to the parent population, the offspring population can include one or more particles. Each particle can be a transformer load distribution scheme, which is used to indicate the load that each distribution transformer in the low-voltage distribution network needs to bear.

[0062] S206: Calculate the population improvement rate of the current iteration round based on the parent population of the current iteration round and the offspring population of the current iteration round.

[0063] Among them, the population improvement rate is used to reflect the improvement degree of the offspring population relative to the parent population in the current iteration round. The population improvement rate is positively correlated with the improvement degree. In other words, the higher the improvement degree, the larger the population improvement rate; conversely, the lower the improvement degree, the smaller the population improvement rate.

[0064] In this step, after updating the population using the CSO algorithm, the computer device can calculate the population improvement rate of the current iteration round based on the parent population of the current iteration round and the offspring population of the current iteration round, so as to evaluate the population improvement degree of the current iteration round, and then judge whether the CSO algorithm has fallen into a local optimal solution.

[0065] When the CSO algorithm has fallen into a local optimum, the improvement degree of the offspring population compared with the parent population is relatively low, resulting in a relatively small population improvement rate. Conversely, when the CSO algorithm has not fallen into a local optimum, the offspring population has made greater progress compared with the parent population, and the population improvement rate is relatively large.

[0066] S208: If the population improvement rate is less than the preset improvement rate threshold, when the preset population initialization rule is satisfied, initialize the parent population of the current iteration round, and use the initialized parent population as the parent population of the next iteration round, and enter the next iteration round.

[0067] Among them, the population initialization rule refers to the rule for determining whether to perform population initialization. The specific content of the rule can be determined according to the actual situation, and this application does not make specific restrictions on this. In some examples, the population initialization rule can be that the number of population initializations is less than or equal to a preset initialization number threshold. If the number of population initializations is less than or equal to the initialization number threshold, it can be determined that the population initialization rule is satisfied; if the number of population initializations is greater than the initialization number threshold, it can be determined that the population initialization rule is not satisfied. In this way, the algorithm can be prevented from falling into an infinite loop, thereby improving the operation efficiency and reliability of the algorithm.

[0068] In this step, if the population improvement rate of the current iteration round is less than or equal to the preset improvement rate threshold, it indicates that in the current iteration round, the improvement degree of the offspring population compared to the parent population is low, and the CSO algorithm may have fallen into a local optimal solution, and the relative position relationship between samples may have inhibited the search process.

[0069] In this case, the computer device can discard the offspring population of the current iteration round in a timely manner to abandon the poor improvement direction with a small improvement rate. And the computer device can determine whether the current population initialization rule is satisfied. If the population initialization rule is satisfied, the parent population of the current iteration round can be initialized to randomly redistribute the domains of all samples and change the relative position relationship between samples. After the population initialization is completed, the computer device can use the initialized parent population as the parent population of the next iteration round and enter the next iteration round. In this way, the CSO algorithm can update the population in the direction with a large improvement rate, and thus a transformer load distribution scheme with the minimum distribution network loss can be obtained.

[0070] For example, if the parent population of the current iteration round is the first population and the offspring population of the current iteration round is the second population, when the population improvement rate is less than the preset improvement rate threshold and the preset population initialization rule is satisfied, the computer device can initialize the first population to obtain a third population and use the third population as the parent population of the next iteration round.

[0071] S210: If the population improvement rate is less than the preset improvement rate threshold, and when the population initialization rule is not satisfied and the preset iteration end condition is not satisfied, use the parent population of the current iteration round as the parent population of the next iteration round and enter the next iteration round.

[0072] Among them, the iteration end condition refers to the condition used to determine whether to end the iterative solution, and its specific content can be determined according to the actual situation, and this application does not make specific restrictions on this. In some examples, the iteration end condition can be that the number of iterations is greater than or equal to a preset iteration number threshold. If the current number of iterations is less than the iteration number threshold, it is determined that the iteration end condition is not satisfied; if the current number of iterations is greater than or equal to the iteration number threshold, it is determined that the iteration end condition is satisfied.

[0073] In this step, if the population improvement rate in the current iteration round is less than the preset improvement rate threshold, and the population initialization rule is not satisfied and the iteration end condition is not satisfied, the offspring population in the current iteration round can be discarded, and the parent population in the current iteration round can be directly used as the parent population in the next iteration round, and enter the next iteration round.

[0074] For example, if the parent population in the current iteration round is the first population and the offspring population in the current iteration round is the second population, when the population improvement rate is less than the preset improvement rate threshold, and the population initialization rule and the preset iteration end condition are not satisfied, the computer device can use the first population as the parent population in the next iteration round.

[0075] In this way, the bad improvement direction with a small improvement rate can be avoided in time, so that the CSO algorithm can update the population in the direction with a large improvement rate, and thus the transformer load distribution plan with the minimum distribution network loss can be obtained. At the same time, by retaining the parent population before the update as the parent population in the next iteration round, the algorithm can retain the optimal solution after the previous update in time, avoid repeated optimization of the algorithm, reduce the repeated search process, and is beneficial to improving the algorithm speed and search efficiency.

[0076] S212: If the population improvement rate is less than the preset improvement rate threshold, then when the population initialization rule is not satisfied and the iteration end condition is satisfied, determine the target load rate corresponding to each distribution transformer according to the parent population in the current iteration round, and control the operation of each distribution transformer based on each target load rate.

[0077] In this step, in the case where the population improvement rate in the current iteration round is less than the preset improvement rate threshold, if the iteration end condition is satisfied and the population initialization rule is not satisfied, the computer device can determine the target transformer load distribution plan from the parent population in the current iteration round, and control the operation of each distribution transformer accordingly, so that each distribution transformer can operate at the load rate indicated by the target transformer load distribution plan. In this way, the final load distribution plan can be determined from the optimal solution with a large improvement rate in the previous update.

[0078] In this application, when using the CSO algorithm to optimize the load rate of each distribution transformer and the transmission power of each port in the area, if the population improvement rate in the current iteration round is less than the preset improvement rate threshold, it indicates that the relative position relationship between samples may have inhibited the search process and may fall into a local optimal solution. Therefore, when the population initialization rule is satisfied, this application can initialize the parent population, so that the neighborhoods of all samples will be randomly redistributed, that is, change the relative position relationship between samples, and enter the next iteration round. In this way, perturbations can be added and bad search directions can be avoided in a timely manner, and falling into a local optimal solution can be avoided.

[0079] If the population improvement rate is less than the preset improvement rate threshold and at the same time does not satisfy the population initialization rule and the iteration end condition, the population can not be updated, and the parent population in the current iteration round is used as the parent population in the next iteration round, and enter the next iteration round. In this way, the optimal solution after the previous update can be fully utilized through the backtracking operation, so that the algorithm can be prevented from falling into a local optimal solution and the algorithm can be prevented from repeating the optimization, which helps to improve the solution efficiency.

[0080] In summary, this application introduces the population improvement rate as an evaluation index for the optimization efficiency and quality of the CSO algorithm during iterative calculation, and improves the traditional CSO algorithm accordingly, so that the problem of the algorithm falling into a local optimal solution can be avoided, and then the transformer load rate allocation scheme with the minimum loss can be determined, and the analysis efficiency can be improved. In this way, the line loss management efficiency and quality can be improved, and then the distribution network loss can be reduced to achieve optimization.

[0081] In some embodiments, the parent population in the current iteration round includes m first transformer load distribution schemes, and the offspring population in the current iteration round includes m second transformer load distribution schemes, where m is a preset positive integer.

[0082] Based on the parent population in the current iteration round and the offspring population in the current iteration round, calculating the population improvement rate in the current iteration round includes:

[0083] Step A1: Determine the first optimal load scheme among the m first transformer load distribution schemes, and determine the second optimal load scheme among the m second transformer load distribution schemes.

[0084] Among them, the first optimal load scheme refers to the optimal scheme among the m first transformer load distribution schemes, that is, the optimal particle of the parent population. The second optimal load scheme refers to the optimal scheme among the m second transformer load distribution schemes, that is, the optimal particle of the offspring population.

[0085] It can be understood that the computer device can determine the superiority and inferiority of each particle in the same population in any way, and determine the first optimal load scheme and the second optimal load scheme accordingly. For example, the computer device can determine the superiority and inferiority of particles according to the fitness corresponding to each particle.

[0086] Step A2: Determine the first fitness corresponding to the first optimal load scheme according to the preset distribution network loss objective function.

[0087] Among them, the distribution network loss objective function can be the low-voltage distribution network loss optimization objective function considering port loss and port capacity, and its specific function can be determined according to the actual situation. The computer device can use the preset distribution network loss objective function to determine the fitness of the parent optimal particle and obtain the first fitness.

[0088] Step A3: Determine the second fitness corresponding to the second optimal load scheme according to the distribution network loss objective function.

[0089] In this step, the computer device can use the preset distribution network loss objective function to determine the fitness of the offspring optimal particle and obtain the second fitness.

[0090] Step A4: Calculate the population improvement rate of the current iteration round based on the first fitness and the second fitness.

[0091] In this step, considering that the first fitness reflects the best fitness of the parent population and the second fitness reflects the best fitness of the offspring population, therefore, the improvement degree of the offspring population compared with the parent population can be obtained through the first fitness and the second fitness, that is, the population improvement rate is obtained. In this way, the population improvement rate can be calculated according to the optimal values of the two generations of populations, thereby improving the calculation efficiency of the population improvement rate, and further improving the loss optimization efficiency of the distribution network.

[0092] In some embodiments, calculating the population improvement rate of the current iteration round based on the first fitness and the second fitness includes:

[0093] Calculate the population improvement rate of the current iteration round based on the following expression :

[0094]

[0095] In the formula, is the first optimal load scheme, is the second optimal load scheme, is the first fitness, is the second fitness, is and the Euclidean distance between, is a preset constant.

[0096] In this embodiment, the population improvement rate is calculated through the above expression, so as to further improve the calculation efficiency of the population improvement rate and the loss optimization efficiency of the distribution network.

[0097] In some embodiments, the distribution network loss objective function is:

[0098]

[0099] where is the distribution network loss objective function, D is the number of distribution transformers, is the iron loss of the i-th distribution transformer, is the load rate of the i-th distribution transformer, is the copper loss of the i-th distribution transformer, is the active power loss of the j-th port, is the penalty coefficient, is the received power of the power supply equipment corresponding to the i-th distribution transformer, is the load demand of the power supply equipment corresponding to the i-th distribution transformer.

[0100] In this embodiment, the goal of distribution network loss optimization is to minimize the total loss of each interconnected transformer in the entire area by optimizing the load rate of each distribution transformer. In the actual operation process, both flexible switches and ordinary switches have a certain current-carrying capacity. In order to be closer to the actual application and further reduce the distribution network loss, this application adds the port capacity of multiple ports as a consideration factor to the optimization goal, specifically:

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107] where is the loss of the i-th distribution transformer; is the iron loss of the i-th distribution transformer, with the unit of kw; is the load rate of the i-th distribution transformer; is the copper loss of the i-th distribution transformer, with the unit of kw; is the apparent power output by the i-th distribution transformer; is the capacity of the i-th distribution transformer; is the power flowing through the corresponding port of the i-th distribution transformer. A positive number indicates inflow into the port, and a negative number indicates outflow from the port; is the pre-set maximum power threshold flowing through the port; D is the number of distribution transformers in the area; is the useful power output by the i-th distribution transformer; is the total power load demand of the area; is the total electric power required to be consumed when the equipment to be powered in the pre-set power supply area is working, that is, the power demand for electricity; is the current capacity of the i-th distribution transformer.

[0108]

[0109]

[0110] In the formula, is the active power loss of the j-th AC / DC port; is the first loss coefficient of the j-th AC / DC port; is the second loss coefficient of the j-th AC / DC port; is the third loss coefficient of the j-th AC / DC port; is the per-unit value of the apparent power corresponding to the transmission power passing through the j-th AC / DC port; is the per-unit value of the active power corresponding to the transmission power passing through the j-th AC / DC port; is the per-unit value of the reactive power corresponding to the transmission power passing through the j-th AC / DC port.

[0111] Therefore, the distribution network loss objective function can be:

[0112]

[0113] In the formula, N is the penalty coefficient, which penalizes whether the updated individual meets the load demand constraint, which can greatly improve the population update efficiency. The number of AC / DC ports in the interconnected system is the same as the number of transformers.

[0114] In this way, a regional distribution network loss mathematical model considering the conversion efficiency of multi-port flexible switches can be constructed. Using this as the objective function for solution, a transformer load distribution scheme with smaller losses can be obtained.

[0115] In some embodiments, the multi-port interconnected low-voltage distribution network loss optimization method of the present application may further include the following steps:

[0116] If the population improvement rate is greater than or equal to the preset improvement rate threshold, when the iteration end condition is not met, the offspring population of the current iteration round is used as the parent population of the next iteration round, and the next iteration round is entered.

[0117] In this embodiment, if the population improvement rate is greater than or equal to the preset improvement rate threshold, it indicates that in the current iteration round, the improvement degree of the offspring population compared to the parent population is relatively high, and the CSO algorithm is currently updating the population along the direction with a greater improvement degree. In this case, if the preset iteration end condition is not met, the computer device can use the offspring population of the current iteration round as the parent population of the next iteration round, so that the next iteration round can search along the direction with a higher improvement degree, and thus a transformer load distribution scheme with smaller losses can be obtained.

[0118] For example, if the parent population of the current iteration round is the first population and the offspring population of the current iteration round is the second population, then when the population improvement rate is greater than or equal to the preset improvement rate threshold and the iteration end condition is not met, the second population is used as the parent population of the next iteration round, and the next iteration round is entered.

[0119] In some embodiments, the multi-port interconnected low-voltage distribution network loss optimization method of the present application may further include the following steps:

[0120] If the population improvement rate is greater than or equal to the preset improvement rate threshold, when the iteration end condition is met, the target load rate corresponding to each distribution transformer is determined according to the offspring population of the current iteration round, and each distribution transformer is controlled to operate based on each target load rate.

[0121] In this embodiment, when the population improvement rate of the current iteration round is greater than or equal to the preset improvement rate threshold, if the iteration end condition is met, the computer device can determine the target transformer load distribution scheme from the offspring population of the current iteration round, and control the operation of each distribution transformer accordingly, so that each distribution transformer can operate at the load rate indicated by the target transformer load distribution scheme. In this way, the final load distribution scheme can be determined from the optimal solution with a large improvement rate last time to reduce the loss of the distribution network.

[0122] To facilitate understanding of the solution of the present application, a specific example is described below.

[0123] As Figure 3 shown, this example provides a multi-port interconnected low-voltage distribution network loss optimization method, which specifically includes the following steps:

[0124] S302: Determine the parameters of the CSO algorithm;

[0125] S304: Initialize the number of iterations T, for example, T = 1 can be set;

[0126] S306: Initialize the total population size M and the population size m. For example, M can be set equal to m.

[0127] S308: Initialize the population.

[0128] S310: Calculate the fitness of the population.

[0129] S312: Perform horizontal operations, adopt a greedy strategy to compete with the parent generation, and update the population.

[0130] S314: Perform vertical operations, adopt a greedy strategy to compete with the parent generation, and update the population to obtain the offspring population.

[0131] S316: Calculate the population improvement rate based on the optimal particle of the offspring population and the optimal particle of the parent population.

[0132] S318: Determine whether the improvement rate requirement is met according to the population improvement rate. If not, execute step S320; otherwise, execute step S326.

[0133] S320: Do not update the population, that is, discard the offspring population and retain the parent population.

[0134] S322: Determine whether M is less than or equal to 2m. If so, execute step S324; otherwise, execute step S328.

[0135] S324: Accumulate the total population size M, and after accumulation, execute step S308.

[0136] S326: Use the offspring population as the parent population for the next iteration.

[0137] S328: Determine whether the maximum number of iterations has been reached. If so, execute step S334; otherwise, execute step S330.

[0138] S330: Record the current population size M and the optimal particle in the population. According to the improvement rate criterion, the optimal population size can be further determined, which can further improve the optimization speed and indicate the direction for optimization.

[0139] S332: Accumulate the number of iterations T, and after accumulation, execute step S312.

[0140] S334: Output the optimal allocation plan and control the load rate of each distribution transformer according to the optimal allocation plan.

[0141] This example has at least the following beneficial effects:

[0142] (1)Compared with the traditional single-radiation operation mode of low-voltage distribution networks, the present application proposes a new network structure for low-voltage distribution networks, which can further improve the power supply reliability of low-voltage distribution networks;

[0143] (2)For the first time, the improvement rate is used to evaluate the optimization efficiency and quality of the CSO algorithm, optimize the load rate of distribution transformers in the region, minimize the power grid loss in the region, and improve the efficiency of the power grid loss analysis of interconnected low-voltage distribution networks. At the same time, the intelligent optimization algorithm is used to solve the problem of optimizing the power grid loss of the multi-port interconnected system, which can fill the gap in the current methods for solving the problem of optimizing the power grid loss of the multi-port interconnected system and improve the efficiency of the power grid loss analysis of interconnected low-voltage distribution networks;

[0144] (3)Creatively transform the problem of regional loss optimization into the optimization of the load rate of transformers and the transmission of port loads, obtain the optimal load rate combination of all transformers in the region, and the optimization result is more scientific.

[0145] The following describes the device for optimizing the power grid loss of a multi-port interconnected low-voltage distribution network provided by the embodiments of the present application. The device for optimizing the power grid loss of a multi-port interconnected low-voltage distribution network described below can be correspondingly referred to the method for optimizing the power grid loss of a multi-port interconnected low-voltage distribution network described above.

[0146] In some embodiments, as Figure 4 shown, the present application provides a device 400 for optimizing the power grid loss of a multi-port interconnected low-voltage distribution network, which is applied to a multi-port interconnected low-voltage distribution network including at least two ports and at least two distribution transformers. The device 400 includes:

[0147] A parental population determination module 402, configured to determine the parental population of the current iteration round;

[0148] A CSO update module 404, configured to perform population update using the cross and span optimization algorithm CSO according to the transformer operation data of each distribution transformer, the port operation data of each port, and the parental population of the current iteration round, and obtain the offspring population of the current iteration round;

[0149] An improvement rate calculation module 406, configured to calculate the population improvement rate of the current iteration round based on the parental population and the offspring population of the current iteration round; wherein, the population improvement rate is used to reflect the improvement degree of the offspring population relative to the parental population in the current iteration round; the population improvement rate is positively correlated with the improvement degree;

[0150] The first iteration module 408 is configured to, if the population improvement rate is less than a preset improvement rate threshold, when the preset population initialization rule is satisfied, perform population initialization on the parental population of the current iteration round, use the initialized parental population as the parental population of the next iteration round, and enter the next iteration round;

[0151] The second iteration module 410 is configured to, if the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not satisfied and the preset iteration end condition is not satisfied, use the parental population of the current iteration round as the parental population of the next iteration round, and enter the next iteration round;

[0152] The first transformer control module 412 is configured to, if the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not satisfied and the iteration end condition is satisfied, determine the target load rate corresponding to each distribution transformer according to the parental population of the current iteration round, and control the operation of each distribution transformer based on each target load rate.

[0153] In some embodiments, the parental population of the current iteration round includes m first transformer load distribution schemes, and the offspring population of the current iteration round includes m second transformer load distribution schemes, where m is a preset positive integer. The improvement rate calculation module 406 of the present application includes:

[0154] An optimal load scheme determination unit for determining a first optimal load scheme among the m first transformer load distribution schemes and a second optimal load scheme among the m second transformer load distribution schemes;

[0155] A first fitness calculation unit for determining a first fitness corresponding to the first optimal load scheme according to a preset distribution network loss objective function;

[0156] A second fitness calculation unit for determining a second fitness corresponding to the second optimal load scheme according to the distribution network loss objective function;

[0157] An improvement rate calculation unit for calculating the population improvement rate of the current iteration round based on the first fitness and the second fitness.

[0158] In some embodiments, the improvement rate calculation unit of the present application is configured to calculate the population improvement rate of the current iteration round based on the following expression :

[0159]

[0160] In the formula, is the first optimal load scheme, is the second optimal load scheme, is the first fitness, is the second fitness, is and the Euclidean distance between, is a preset constant.

[0161] In some embodiments, the objective function of the distribution network loss is:

[0162]

[0163] In the formula, is the objective function of the distribution network loss, D is the number of distribution transformers, is the iron loss of the i-th distribution transformer, is the load rate of the i-th distribution transformer, is the copper loss of the i-th distribution transformer, is the active power loss of the j-th port, is the penalty coefficient, is the received power of the power supply equipment corresponding to the i-th distribution transformer, is the load demand of the power supply equipment corresponding to the i-th distribution transformer.

[0164] In some embodiments, the population initialization rule is that the number of population initializations is less than or equal to a preset initialization number threshold; and / or, the iteration end condition is that the number of iterations is greater than or equal to a preset iteration number threshold.

[0165] In some embodiments, the multi-port interconnected low-voltage distribution network loss optimization device 400 of the present application further includes:

[0166] A third iteration module, configured to, if the population improvement rate is greater than or equal to the preset improvement rate threshold, when the iteration end condition is not satisfied, use the offspring population of the current iteration round as the parent population of the next iteration round, and enter the next iteration round.

[0167] In some embodiments, the multi-port interconnected low-voltage distribution network loss optimization device 400 of the present application further includes:

[0168] A second transformer control module, configured to, if the population improvement rate is greater than or equal to the preset improvement rate threshold, when the iteration end condition is satisfied, determine the target load rate corresponding to each distribution transformer according to the offspring population of the current iteration round, and control the operation of each distribution transformer based on each target load rate.

[0169] In one embodiment, the present application further provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to execute the steps of the multi-port interconnected low-voltage distribution network loss optimization method in any embodiment.

[0170] In one embodiment, the present application further provides a computer device storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to execute the steps of the multi-port interconnected low-voltage distribution network loss optimization method in any embodiment.

[0171] Schematically, Figure 5 FIG. is an internal structural diagram of a computer device provided by an embodiment of the present application. In one example, the computer device may be a server. Referring to Figure 5 , the computer device 900 includes a processing component 902, which further includes one or more processors, and memory resources represented by a memory 901 for storing instructions executable by the processing component 902, such as application programs. The application programs stored in the memory 901 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 902 is configured to execute instructions to perform the steps of the method described in any of the above embodiments.

[0172] The computer device 900 may further include a power supply component 903 configured to perform power management of the computer device 900, a wired or wireless network interface 904 configured to connect the computer device 900 to a network, and an input / output (I / O) interface 905. The computer device 900 may operate based on an operating system stored in the memory 901, such as WindowsServer TM, Mac OS XTM, Unix TM, Linux TM, Free BSDTM or the like.

[0173] Those skilled in the art can understand that the internal structure of the computer device shown in the present application is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0174] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element. In this text, "a", "an", "the", "this" and "its" may also include the plural form, unless the context clearly indicates otherwise. A plurality means at least two cases, such as 2, 3, 5 or 8, etc. "And / or" includes any and all combinations of the related listed items.

[0175] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0176] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the losses of a multi-port interconnected low-voltage distribution network, characterized in that, The multi-port interconnected low-voltage distribution network includes at least two ports and at least two distribution transformers, and the method includes: Determine the parental population of the current iteration round; the parental population of the current iteration round includes m first transformer load distribution schemes, where m is a preset positive integer; According to the transformer operation data of each distribution transformer, the port operation data of each port, and the parental population of the current iteration round, use the cross-swarm optimization algorithm CSO to update the population and obtain the offspring population of the current iteration round; the offspring population of the current iteration round includes m second transformer load distribution schemes; Based on the parental population of the current iteration round and the offspring population of the current iteration round, calculate the population improvement rate of the current iteration round; wherein, the population improvement rate is used to reflect the improvement degree of the offspring population relative to the parental population in the current iteration round; the population improvement rate is positively correlated with the improvement degree; If the population improvement rate is less than the preset improvement rate threshold, when the preset population initialization rule is satisfied, initialize the parental population of the current iteration round, and use the initialized parental population as the parental population of the next iteration round, and enter the next iteration round; If the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not satisfied and the preset iteration end condition is not satisfied, use the parental population of the current iteration round as the parental population of the next iteration round, and enter the next iteration round; If the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not satisfied and the iteration end condition is satisfied, determine the target load rate corresponding to each distribution transformer according to the parental population of the current iteration round, and control the operation of each distribution transformer based on each target load rate; Among them, calculating the population improvement rate of the current iteration round based on the parental population of the current iteration round and the offspring population of the current iteration round includes: Determine the first optimal load scheme among the m first transformer load distribution schemes, and determine the second optimal load scheme among the m second transformer load distribution schemes; According to the preset distribution network loss objective function, determine the first fitness corresponding to the first optimal load scheme; According to the distribution network loss objective function, determine the second fitness corresponding to the second optimal load scheme; The population improvement rate of the current iteration round is calculated based on the following expression :[[]]END]] ； In the formula, is the first optimal load scheme, is the second optimal load scheme, is the first fitness, is the second fitness, is the Euclidean distance between and is a preset constant.

2. The method according to claim 1, characterized in that, The distribution network loss objective function is: ； In the formula, is the objective function of the distribution network loss, D is the number of distribution transformers, is the iron loss of the i-th distribution transformer, is the load factor of the i-th distribution transformer, is the copper loss of the i-th distribution transformer, is the active power loss of the j-th port, is the penalty coefficient, is the received power of the power supply equipment corresponding to the i-th distribution transformer, is the load demand of the power supply equipment corresponding to the i-th distribution transformer.

3. The method according to claim 1, characterized in that, The population initialization rule is that the number of population initialization times is less than or equal to the preset initialization times threshold; and / or, the iteration end condition is that the number of iteration times is greater than or equal to the preset iteration times threshold.

4. The method according to any one of claims 1 to 3, characterized in that The method further includes: If the population improvement rate is greater than or equal to the preset improvement rate threshold, when the iteration end condition is not satisfied, use the offspring population of the current iteration round as the parental population of the next iteration round, and enter the next iteration round.

5. The method according to any one of claims 1 to 3, characterized in that The method further includes: If the population improvement rate is greater than or equal to the preset improvement rate threshold, when the iteration end condition is satisfied, determine the target load rate corresponding to each distribution transformer according to the offspring population of the current iteration round, and control the operation of each distribution transformer based on each target load rate respectively.

6. A multi-port interconnected low-voltage distribution network loss optimization device, characterized in that, The multi-port interconnected low-voltage distribution network includes at least two ports and at least two distribution transformers. The device includes: A parental population determination module, configured to determine the parental population of the current iteration round; the parental population of the current iteration round includes m first transformer load distribution schemes, where m is a preset positive integer; A CSO update module, configured to perform population update on the basis of the transformer operation data of each distribution transformer, the port operation data of each port, and the parental population of the current iteration round by using the cross-swarm optimization algorithm CSO, and obtain the offspring population of the current iteration round; the offspring population of the current iteration round includes m second transformer load distribution schemes; An improvement rate calculation module, configured to calculate the population improvement rate of the current iteration round based on the parental population and the offspring population of the current iteration round; wherein, the population improvement rate is used to reflect the improvement degree of the offspring population relative to the parental population in the current iteration round; the population improvement rate is positively correlated with the improvement degree; A first iteration module, configured to, if the population improvement rate is less than the preset improvement rate threshold, when the preset population initialization rule is satisfied, perform population initialization on the parental population of the current iteration round, and use the initialized parental population as the parental population of the next iteration round, and enter the next iteration round; A second iteration module, configured to, if the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not satisfied and the preset iteration end condition is not satisfied, use the parental population of the current iteration round as the parental population of the next iteration round, and enter the next iteration round; A first transformer control module, configured to, if the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not satisfied and the iteration end condition is satisfied, determine the target load rate corresponding to each distribution transformer according to the parental population of the current iteration round, and control the operation of each distribution transformer based on each target load rate respectively; Wherein, the improvement rate calculation module includes: An optimal load scheme determination unit, configured to determine a first optimal load scheme among the m first transformer load distribution schemes, and determine a second optimal load scheme among the m second transformer load distribution schemes; A first fitness calculation unit, configured to determine a first fitness corresponding to the first optimal load scheme according to a preset distribution network loss objective function; A second fitness calculation unit, configured to determine a second fitness corresponding to the second optimal load scheme according to the distribution network loss objective function; An improvement rate calculation unit for calculating the population improvement rate of the current iteration round based on the following expression : ； In the formula, is the first optimal load scheme, is the second optimal load scheme, is the first fitness, is the second fitness, is the Euclidean distance between and is a preset constant.

7. A storage medium, characterized in that, The storage medium stores computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to execute the steps of the multi-port interconnected low-voltage distribution network loss optimization method according to any one of claims 1 to 5.

8. A computer device, characterized in that, Including: One or more processors, and a memory; The memory stores computer-readable instructions, which, when executed by the one or more processors, execute the steps of the multi-port interconnected low-voltage distribution network loss optimization method according to any one of claims 1 to 5.

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